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Intro to Data Mining

Author: Pang-Ning Tan; Michael Steinbach; Vipin Kumar
Publisher: New Jersey : P.Ed Australia, 2005.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Intro to Data Mining.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Tan, Pang-Ning
Intro to Data Mining
New Jersey : P.Ed Australia,c2005
Material Type: Document
Document Type: Book, Computer File
All Authors / Contributors: Pang-Ning Tan; Michael Steinbach; Vipin Kumar
ISBN: 9781488666865 1488666865
OCLC Number: 1028222142
Notes: Description based upon print version of record.
4.6.3: Comparing the Performance of Two Classifiers
Description: 1 online resource (792 p.)
Contents: Cover; Dedication; Preface; Contents; Chapter 1: Introduction; 1.1: What Is DataMining?; 1.2: Motivating Challenges; 1.3: The Origins of Data Mining; 1.4: DataMining Tasks; 1.5: Scope and Organization of the Book; 1.6: Bibliographic Notes; 1.7: Exercises; Chapter 2: Data; 2.1: Types of Data; 2.1.1: Attributes and Measurement; 2.1.2: Types of Data Sets; 2.2: Data Quality; 2.2.1: Measurement and Data Collection Issues; 2.2.2: Issues Related to Applications; 2.3: Data Preprocessing; 2.3.1: Aggregation; 2.3.2: Sampling; 2.3.3: Dimensionality Reduction; 2.3.4: Feature Subset Selection 2.3.5: Feature Creation2.3.6: Discretization and Binarization; 2.3.7: Variable Transformation; 2.4: Measures of Similarity and Dissimilarity; 2.4.1: Basics; 2.4.2: Similarity and Dissimilarity between Simple Attributes; 2.4.3: Dissimilarities between Data Objects; 2.4.4: Similarities between Data Objects; 2.4.5: Examples of Proximity Measures; 2.4.6: Issues in Proximity Calculation; 2.4.7: Selecting the Right Proximity Measure; 2.5: Bibliographic Notes; 2.6: Exercises; Chapter 3: Exploring Data; 3.1: The Iris Data Set; 3.2: Summary Statistics; 3.2.1: Frequencies and the Mode 3.2.2: Percentiles3.2.3: Measures of Location: Mean and Median; 3.2.4: Measures of Spread: Range and Variance; 3.2.5: Multivariate Summary Statistics; 3.2.6: Other Ways to Summarize the Data; 3.3: Visualization; 3.3.1: Motivations for Visualization; 3.3.2: General Concepts; 3.3.3: Techniques; 3.3.4: Visualizing Higher-Dimensional Data; 3.3.5: Doâ#x80;#x99;s and Donâ#x80;#x99;ts; 3.4: OLAP andMultidimensional Data Analysis; 3.4.1: Representing Iris Data as a Multidimensional Array; 3.4.2: Multidimensional Data: The General Case; 3.4.3: Analyzing Multidimensional Data 3.4.4: Final Comments on Multidimensional Data Analysis3.5: Bibliographic Notes; 3.6: Exercises; Chapter 4: Classification: Basic Concepts, Decision Trees, and Model Evaluation; 4.1: Preliminaries; 4.2: General Approach to Solving a Classification Problem; 4.3: Decision Tree Induction; 4.3.1: How a Decision Tree Works; 4.3.2: How to Build a Decision Tree; 4.3.3: Methods for Expressing Attribute Test Conditions; 4.3.4: Measures for Selecting the Best Split; 4.3.5: Algorithm for Decision Tree Induction; 4.3.6: An Example: Web Robot Detection; 4.3.7: Characteristics of Decision Tree Induction 4.4: Model Overfitting4.4.1: Overfitting Due to Presence of Noise; 4.4.2: Overfitting Due to Lack of Representative Samples; 4.4.3: Overfitting and the Multiple Comparison Procedure; 4.4.4: Estimation of Generalization Errors; 4.4.5: Handling Overfitting in Decision Tree Induction; 4.5: Evaluating the Performance of a Classifier; 4.5.1: Holdout Method; 4.5.2: Random Subsampling; 4.5.3: Cross-Validation; 4.5.4: Bootstrap; 4.6: Methods for Comparing Classifiers; 4.6.1: Estimating a Confidence Interval for Accuracy; 4.6.2: Comparing the Performance of Two Models

Abstract:

Intro to Data Mining.

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